Open World Classification: [1]} first proposed the open world setting for image recognition. Instead of a static classifier trained on a fixed set of classes, they proposed a more flexible setting where knowns and unknowns both coexist. The model can recognize both types of objects and adaptively improve itself when new labels for unknown are provided. Their approach extends Nearest Class Mean classifier to operate in an open world setting by re-calibrating the class probabilities to balance open space risk. [2]} studies open world face identity learning while [3]} proposed to use an exemplar set of seen classes to match them against a new sample, and rejects it in case of a low match with all previously known classes.
However, they don’t test on image classification benchmarks and study product classification in e-commerce applications.
